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Fire smoke detection in petrochemical plant can prevent fire and ensure production safety and life safety. The purpose of this paper is to solve the problem of missed detection and false detection in flame smoke detection under complex factory background.

This paper presents a flame smoke detection algorithm based on YOLOv5. The target regression loss function (CIoU) is used to improve the missed detection and false detection in target detection and improve the model detection performance. The improved activation function avoids gradient disappearance to maintain high real-time performance of the algorithm. Data enhancement technology is used to enhance the ability of the network to extract features and improve the accuracy of the model for small target detection.

Based on the actual situation of flame smoke, the loss function and activation function of YOLOv5 model are improved. Based on the improved YOLOv5 model, a flame smoke detection algorithm with generalization performance is established. The improved model is compared with SSD and YOLOv4-tiny. The accuracy of the improved YOLOv5 model can reach 99.5%, which achieves a more accurate detection effect on flame smoke. The improved network model is superior to the existing methods in running time and accuracy.

Aiming at the actual particularity of flame smoke detection, an improved flame smoke detection network model based on YOLOv5 is established. The purpose of optimizing the model is achieved by improving the loss function, and the activation function with stronger nonlinear ability is combined to avoid over-fitting of the network. This method is helpful to improve the problems of missed detection and false detection in flame smoke detection and can be further extended to pedestrian target detection and vehicle running recognition.

The similarity measurement of time series is an important research in time series detection, which is a basic work of time series clustering, anomaly discovery, prediction and many other data mining problems. The purpose of this paper is to design a new similarity measurement algorithm to improve the performance of the original similarity measurement algorithm. The subsequence morphological information is taken into account by the proposed algorithm, and time series is represented by a pattern, so the similarity measurement algorithm is more accurate.

Following some previous researches on similarity measurement, an improved method is presented. This new method combines morphological representation and dynamic time warping (DTW) technique to measure the similarities of time series. After the segmentation of time series data into segments, three parameter values of median, point number and slope are introduced into the improved distance measurement formula. The effectiveness of the morphological weighted DTW algorithm (MW-DTW) is demonstrated by the example of momentum wheel data of an aircraft attitude control system.

The improved method is insensitive to the distortion and expansion of time axis and can be used to detect the morphological changes of time series data. Simulation results confirm that this method proposed in this paper has a high accuracy of similarity measurement.

This improved method has been used to solve the problem of similarity measurement in time series, which is widely emerged in different fields of science and engineering, such as the field of control, measurement, monitoring, process signal processing and economic analysis.

In the similarity measurement of time series, the distance between sequences is often used as the only detection index. The results of similarity measurement should not be affected by the longitudinal or transverse stretching and translation changes of the sequence, so it is necessary to incorporate the morphological changes of the sequence into similarity measurement. The MW-DTW is more suitable for the actual situation. At the same time, the MW-DTW algorithm reduces the computational complexity by transforming the computational object to subsequences.

Abnormal changes in temperature directly affect the stability and reliability of a gyroscope. Predicting the temperature and detecting the abnormal change is great value for timely understanding of the working state of the gyroscope. Considering that the actual collected gyroscope shell temperature data have strong non-linearity and are accompanied by random noise pollution, the prediction accuracy and convergence speed of the traditional method need to be improved. The purpose of this paper is to use a predictive model with strong nonlinear mapping ability to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change.

In this paper, an double hidden layer long-short term memory (LSTM) is presented to predict temperature data for the gyroscope (including single point and period prediction), and the evaluation index of the prediction effect is also proposed, and the prediction effects of shell temperature data are compared by BP network, support vector machine (SVM) and LSTM network. Using the estimated value detects the abnormal change of the gyroscope.

By combined simulation calculation with the gyroscope measured data, the effect of different network hyperparameters on shell temperature prediction of the gyroscope is analyzed, and the LSTM network can be used to predict the temperature (time series data). By comparing the performance indicators of different prediction methods, the accuracy of the shell temperature estimation by LSTM is better, which can meet the requirements of abnormal change detection. Quick and accurate diagnosis of different types of gyroscope faults (steps and drifts) can be achieved by setting reasonable data window lengths and thresholds.

The LSTM model is a deep neural network model with multiple non-linear mapping levels, and can abstract the input signal layer by layer and extract features to discover deeper underlying laws. The improved method has been used to solve the problem of strong non-linearity and random noise pollution in time series, and the estimated value can detect the abnormal change of the gyroscope.

In this paper, based on the LSTM network, an double hidden layer LSTM is presented to predict temperature data for the gyroscope (including single point and period prediction), and validate the effectiveness and feasibility of the algorithm by using shell temperature measurement data. The prediction effects of shell temperature data are compared by BP network, SVM and LSTM network. The LSTM network has the best prediction effect, and is used to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change.

The purpose of this paper is to present research in detecting and identifying abrupt faults in controlled auto‐regressive (CAR) processes.

Model‐based approach is adopted in this paper. Two series of fault‐tolerant iterative estimators are set up to estimate online the coefficients in a CAR process. Based on these fault‐tolerant estimators, the detailed detecting and identifying algorithms are obtained for not only the pulse‐type faults but also the step‐type faults in CAR process.

This paper illustrates the useful information that can be obtained from residuals and that can be used to detect pulse‐type faults as well as step‐type faults. A fault‐tolerant recursive estimator for the coefficients of the CAR process is put forward. Using a simple transformation from step‐ to pulse‐type faults, all kinds of diagnosis methods to detect and identify step‐type faults can be used.

Fault‐tolerant estimators and fault detection and identification algorithms are aimed at abrupt faults in CAR processes.

Most of the algorithms given in this paper can be used in many different fields, such as process monitoring, safety control and change detection, etc.

This paper contributes to research of abrupt faults and abrupt changes in a CAR process and emphasizes identification of magnitudes of abrupt faults. The fault‐tolerant estimators are effective not only to detect faults but also to identify safely the coefficients CAR model.

This research outlines the Hong Kong film industry with examination of key actors, directors, films, and production companies within the martial arts genre of Hong Kong Action Cinema. Hong Kong Film Award winners and nominees, core films within genres, and core reference works both general and theoretical from experts in the field of Hong Kong martial arts film research have been highlighted. Web sites are suggested that provide reviews of Hong Kong martial arts films, biographical information on a variety of actors and actresses as well as comprehensive bibliographic information on select films. Also included are commercial Web sites that provide Hong Kong martial arts films.

The effect of different service parameters on the current-carrying tribological properties of CF-Al₂O₃/Cu composites was investigated, and the damage behavior of the composites under different service parameters was probed. The purpose of this study is to provide a theoretical basis for the application of CF-Al₂O₃/Cu composites.

The composites were fabricated by internal oxidation combined with powder metallurgy. The current-carrying tribological properties of CF-Al₂O₃/Cu composites were investigated on an electrical damage test system at different loads and currents.

As the load increases, the wear mechanism of the composite changes from abrasive wear to delamination wear. As the current increases, the oxidation wear and arc erosion of the composites gradually intensified. Under the service parameters of 0–25 A and 30–40 N, the composite has relatively stable current-carrying tribological properties.

This paper could provide a theoretical basis for the practical application of CF-Al₂O₃/Cu composites.

The current study aims at exploring China’s tourism sustainability issues as indicated in magazine articles related to China tourism. China’s environmental, socio-cultural and economic sustainability has been frequently discussed in Western media.

Both qualitative and quantitative research methodologies were used to content analyze the textual data in one of the most popular US travel magazine – National Geographic in the past ten years (2003-2012).

This study presents the changes of topics on China tourism image conveyed by Western media in the past ten years (2013-2012). In addition, themes on China tourism sustainability issues are extracted from the China tourism image messages. The results reveal China tourism sustainability issues in two levels: sustainability issues directly related to China tourism with two dimensions of environmental sustainability and socio-cultural sustainability, and sustainability issues indirectly related to China tourism with two dimensions of economic sustainability and socio-cultural sustainability. Furthermore, the results delineate both positive and negative observations on environmental, socio-cultural and economic sustainability.

The findings of the study highlight challenges and successful practices of the sustainable development in China tourism destinations and the importance of incorporating tourism image in sustainable practices, as well as a need of better communication with foreign media.

This study aims to offer a comprehensive exploration of the potential and challenges associated with sensor fusion-based virtual reality (VR) applications in the context of enhanced physical training. The main objective is to identify key advancements in sensor fusion technology, evaluate its application in VR systems and understand its impact on physical training.

The research initiates by providing context to the physical training environment in today’s technology-driven world, followed by an in-depth overview of VR. This overview includes a concise discussion on the advancements in sensor fusion technology and its application in VR systems for physical training. A systematic review of literature then follows, examining VR’s application in various facets of physical training: from exercise, skill development and technique enhancement to injury prevention, rehabilitation and psychological preparation.

Sensor fusion-based VR presents tangible advantages in the sphere of physical training, offering immersive experiences that could redefine traditional training methodologies. While the advantages are evident in domains such as exercise optimization, skill acquisition and mental preparation, challenges persist. The current research suggests there is a need for further studies to address these limitations to fully harness VR’s potential in physical training.

The integration of sensor fusion technology with VR in the domain of physical training remains a rapidly evolving field. Highlighting the advancements and challenges, this review makes a significant contribution by addressing gaps in knowledge and offering directions for future research.

The purpose of this paper is to reargue the great controversies surrounding marketization, by the concept of intrinsic value and to give a panorama of China's marketization and social development during last 30 years.

The concept of intrinsic value is elaborated, and marketization and closely linked concepts such as price, wealth, happiness, freedom, etc. are reargued. An intrinsic value account (IVA) is constructed and the enhancement and weakening of some intrinsic values could be clearly shown. A panorama of China's marketization and social development is exhibited by IVA.

The basic point of this paper is that social development is a concept with strong but implicit ethical assumption and should be explicitly based upon intrinsic value. Without concrete definition and consensus on intrinsic value, it will face great disputes on judging human history as social development or social degradation. Market is not only an objective value‐neural system, but a subjective moral entity. China's maketization has enhanced economic value greatly, but suffered great loss by other intrinsic values.

IVA could be a practical instrument to evaluate social development and clarify the possible academic controversies on market. The arguments of China's marketization experience could be of benefit to other developing countries.

This paper could encourage people, particularly policy makers, to consider their value assumptions, value priority, some basic concepts in market, and what human kind is really pursuing.

The paper shows that IVA could be a new instrument to better evaluate social development, similar to the National Income Account or National Happiness Account.

Metaverse technology is of interest to researchers and practitioners in tourism and hospitality. The metaverse offers tourists the capacity to enjoy unprecedented tourism experiences. These opportunities may radically change the tourism and hospitality landscape. This study aims to examine how metaverse experiences affect tourists' cognitive processing, satisfaction and loyalty toward metaverse tourism using the stimulus-organism-response paradigm and cognitive experiential self-theory.

An online survey was undertaken to gather information from 329 respondents who were frequent users of different metaverse applications to meet the study's objectives. Data were evaluated through partial least squares structural equation modelling.

The findings revealed that metaverse experiences, such as immersion, escapism and enjoyment, had a significant positive impact on metaverse organismic experience. Additionally, metaverse cognitive processing positively influenced metaverse tourist responses, including satisfaction and loyalty, with tourist involvement acting as a moderator.

This study provides guidelines for metaverse developers and travel industry professionals to encourage visitor satisfaction and loyalty to metaverse tourism.

Metaverse 技术引起了旅游和酒店业的研究人员和从业人员的兴趣。 元宇宙为游客提供了享受前所未有的旅游体验的能力。 这些机会可能会从根本上改变旅游业和酒店业的格局。 本研究旨在使用刺激-有机体-反应 (S-O-R) 范式和认知体验自我理论 (CEST) 研究虚拟世界体验如何影响游客的认知处理、满意度和对虚拟世界旅游的忠诚度。

为了实现研究目标, 我们开展了一项在线调查, 从 329 名经常使用不同元宇宙应用程序的受访者那里收集信息。 通过偏最小二乘结构方程模型 (PLS-SEM) 评估数据。

研究结果表明, 虚拟世界体验, 如沉浸、逃避现实和享受, 对虚拟世界有机体体验有显着的积极影响。 此外, 虚拟世界的认知处理对虚拟世界游客的反应产生了积极影响, 包括满意度和忠诚度, 游客的参与起到了调节作用。

这项研究为元宇宙开发人员和旅游行业专业人士提供指导方针, 以鼓励游客对元宇宙旅游的满意度和忠诚度。

El Metaverso esta siendo de de interés para investigadores y profesionales en turismo y hotelería. Este avance tecnológico ofrece al turista la capacidad de disfrutar de experiencias turísticas inéditas, generando oportunidades que pueden cambiar radicalmente el panorama de la industria del turismo. Este estudio tiene la intención de examinar cómo las experiencias del metaverso afectan el procesamiento cognitivo, la satisfacción y la lealtad de los turistas hacia el turismo del metaverso utilizando el paradigma de estímulo-organismo-respuesta (S-O-R) y la auto-teoría cognitiva experiencial (CEST).

Se realizó una encuesta en línea para recopilar información de 329 encuestados que eran usuarios frecuentes de diferentes aplicaciones de metaverso para cumplir con los objetivos del estudio. Los datos se evaluaron a través de modelos de ecuaciones estructurales de mínimos cuadrados parciales (PLS-SEM).

Los hallazgos revelaron que las experiencias del metaverso, como la inmersión, el escapismo y el disfrute, tuvieron un impacto positivo significativo en la experiencia organísmica del metaverso. Además, el procesamiento cognitivo del metaverso influyó positivamente en las respuestas de los turistas del metaverso, incluidas la satisfacción y la lealtad, y la participación del turista actuó como moderador.

Este estudio proporciona pautas para los desarrolladores de metaversos y los profesionales de la industria de viajes para fomentar la satisfacción y la lealtad de los visitantes al turismo de metaversos.